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Article
Get started with generative AI assets in watsonx
Use this tutorial quick guide to work with and govern your generative AI assets
On this page
This series is designed for data engineers, data scientists, machine learning engineers, and prompt engineers who are part of a team that's building machine learning and generative AI assets. It provides quick links to relevant generative AI tutorials.
Log in to IBM watsonx
If you would like to follow along with the tutorials listed here, you should have an IBM watsonx account.
- If you have a watsonx account, then visit the watsonx platform.
- If you do not have a personal account or an account with your organization, visit the watsonx platform, and click Sign up and try for free. Or, you can visit Try IBM watsonx to sign up.
Working with generative AI
Basic tasks
To get started with working with generative AI, you should understand the overall workflow, choose a tutorial, and check out other learning resources for working on the platform.
Your prompt engineering workflow has the following basic steps.
Open your sandbox project that was automatically created for you.
If necessary, create the service instance that provides the tool that you want to use and associate it with the project.
Choose a tool to prompt foundation models. Each of the tutorials describes a tool.
Save and share your best prompts.
Tutorials for working with generative AI
Each tutorial provides a description of the tool, a video, the instructions, and additional learning resources.
| Tutorial | Description | Expertise for tutorial |
|---|---|---|
| Prompt a foundation model using Prompt Lab | Experiment with prompting different foundation models, explore sample prompts, and save and share your best prompts. | Prompt a model using Prompt Lab without coding. Beginner, No code |
| Prompt a foundation model with the retrieval-augmented generation pattern | Prompt a foundation model by leveraging information in a knowledge base. | Use the retrieval-augmented generation pattern in a Jupyter Notebook that uses Python code. Intermediate, All code |
| Tune a foundation model | Tune a foundation model to enhance model performance. | Use the Tuning Studio to tune a model without coding. Intermediate, No code |
| Try the watsonx.ai end-to-end use case | Follow a use case from data preparation through prompt engineering. | Use various tools, such as notebooks and Prompt Lab. Intermediate, All code |
Governing AI
Basic tasks
To get started with governing AI, you should understand the overall workflow, choose a tutorial, and check out other learning resources for working on the platform.
Your AI governance workflow has the following basic steps.
Open your sandbox project that was automatically created for you.
If necessary, create the service instance that provides the tool that you want to use and associate it with the project.
Choose a tool to govern AI. Each of the tutorials describes a tool.
Tutorials for governing AI
Each tutorial provides a description of the tool, a video, the instructions, and additional learning resources:
| Tutorial | Description | Expertise for tutorial |
|---|---|---|
| Evaluate and track a prompt template | Evaluate a prompt template to measure the performance of a foundation model and track the prompt template through its lifecycle. | Use the evaluation tool and an AI use case to track the prompt template. Beginner, No code |
| Evaluate a machine learning model | Deploy a model, configure monitors for the deployed model, and evaluate the model. | Run a notebook to configure the models and use Watson OpenScale to evaluate. Intermediate, Low code |
Next steps
Documentation, videos, and tutorials
- Visit the watsonx documentation.
- Find more Quick start tutorials in the watsonx documentation.
- Find a comprehensive set of videos that show many common tasks in watsonx.
Learn more
- Overview of watsonx
- watsonx use cases
- Retrieval-augmented generation pattern
- Prompt Lab
- Foundation models
- Governing assets with watsonx.governance
- Evaluating AI models
- Governing assets in AI use cases
Samples
Find sample data sets, projects, models, prompts, and notebooks in the Resource Hub area to gain hands-on experience.
Notebooks that you can add to your project to get started analyzing data and building models.
Projects that you can import that contain notebooks, data sets, prompts, and other assets.
Data sets that you can add to your project to refine, analyze, and build models.
Prompts that you can use in the Prompt Lab to prompt a foundation model.
Foundation models that you can use in the Prompt Lab.